Category Archives: digital

August 21, 2026 · 10:59 am

Call for papers: Strategic governance and ecosystem management in AI-enabled digital marketplaces

Deadline: 10th July 2027

Journal

Journal of Strategy and Management

Guest editor(s)

Ciro Troise, Mark Anthony Camilleri, Mohamed Hani Gheith, Jacopo Ballerini

Introduction

The special issue will focus on issues surrounding strategic governance and ecosystem management in AI-enabled digital marketplaces. Digital marketplaces have become central infrastructures through which firms design, implement, and revise competitive strategies. Across industries, platforms such as Amazon, Alibaba, and Booking.com increasingly shape how firms access markets, coordinate interactions, and capture value. These marketplaces are no longer simply transactional venues: they operate as strategic environments in which rules, data, and intermediation structures redefine the competitive landscape and reshape ecosystem-level outcomes (Bourai et al., 2024; Loonam & O’Regan, 2022). A key reason for the strategic relevance of marketplaces is that they embed governance directly into market processes. Pricing rules, commission systems, access regimes, ranking and recommendation logics, and enforcement routines influence participation incentives and competitive conduct. Marketplace governance thus becomes a strategic design issue rather than an operational detail. This aligns with research conceptualizing platforms as hybrid governance systems that blend market coordination, hierarchical control, and network interdependence, where governance mechanisms continuously evolve in response to ecosystem dynamics (Cuypers et al., 2021; McIntyre et al., 2020). This governance perspective also brings boundary decisions and distribution strategy back to the center of strategic management. For vendors—particularly SMEs—marketplace participation is a fundamental strategic choice affecting dependence, autonomy, capability development, and long-term positioning. Firms increasingly experiment with direct, indirect, and hybrid routes to market, integrating proprietary infrastructures with third-party marketplaces and offline channels. Recent evidence suggests that SME performance depends on how platform adoption interacts with commitment and organizational routines (Ballerini et al., 2023). These choices reflect strategic trade-offs between control and efficiency and are shaped by how firms design and govern multichannel systems (Homburg et al., 2020). Marketplace governance is also deeply intertwined with organizational learning, alliances, and transformation processes. Digital strategy effectiveness depends on cultural and organizational alignment (Cyfert et al., 2025), while dynamic capabilities shape firms’ ability to adapt and transform in digital contexts (Ellström et al., 2022). In turbulent environments, SMEs rely on agility and transformation capabilities to remain competitive (Troise et al., 2022). Moreover, alliances and tacit learning can enable recovery and resilience under constrained governance conditions, particularly in emerging markets (Aditchere et al., 2025). These dynamics highlight that governance is not only imposed by platforms but also shaped by how ecosystem actors learn, adapt, and coordinate over time (Öberg, 2024). At the same time, governance complexity is increasingly amplified by AI-enabled systems embedded in marketplace architectures. Algorithmic ranking, automated monitoring, fraud detection, dynamic pricing, and AI-supported customer management systems increasingly mediate competitive interactions and decision-making. Recent research suggests that generative AI can enhance market effectiveness through CRM-related applications, particularly under technological turbulence and with strong top management support (Kumar et al., 2025). Yet adoption is shaped by managerial cognition and organizational culture: technophobia, self-regulated learning, and open cultures influence managerial intentions to adopt generative AI (Zhao et al., 2025). Marketplace-based business models may also create competitive advantage by reducing time-related frictions for ecosystem participants, reinforcing the strategic role of platform architectures in enabling efficiency and coordination (Santoro et al., 2025). In practice, major marketplaces have already introduced generative AI tools for sellers and advertising optimization (e.g., Amazon’s AI listing tools and ad automation), illustrating how AI is increasingly integrated into governance and ecosystem management logics. These developments raise not only managerial and strategic issues but also broader societal and ethical questions. AI-enabled governance may increase efficiency and scalability, yet it may also intensify opacity, reinforce asymmetries, and raise concerns around accountability, contestability, and fairness. Understanding how governance mechanisms evolve under AI-enabled coordination therefore represents a timely and consequential research agenda. In addition, digital marketplace configurations may shape firm growth and internationalization trajectories in ways that depend on the strategic balance between platform reach and strategic autonomy (Ballerini et al., 2024).

We welcome submissions to this special issue. The special issue seeks a mix of theoretical, conceptual, and empirical cases and is open to various methods (e.g., qualitative case studies, quantitative analysis of platform data, or formal modeling, etc.). It welcomes theoretically grounded and empirically rich contributions that advance strategy and management research on governance and ecosystem management in AI-enabled digital marketplaces. We encourage conceptual and theory-building contributions, qualitative and process-based studies, large-scale empirical analyses (including digital trace and platform data), mixed-method research, and comparative cross-country work. In line with emerging methodological developments, we also explicitly welcome innovative approaches such as agent-based modeling, longitudinal ecosystem mapping, and multi-level designs that connect governance mechanisms to ecosystem-level outcomes. Topics covered include (but are not limited to):

1. Designing and revising governance architectures in digital marketplaces

  • How governance systems are deliberately designed, experimented with, and revised over time
  • Strategic trade-offs between openness, control, and scalability across platform life cycles
  • Emergent governance practices beyond formal rules (e.g., informal coordination, adaptive enforcement, negotiated control)
  • AI-driven enforcement and adaptive governance (e.g., automated compliance monitoring, fraud detection, dynamic rule implementation)

2. Boundary choices and the strategic reconfiguration of distribution ecosystems

  • How firms navigate shifting boundaries between proprietary infrastructures, third-party marketplaces, and hybrid configurations
  • Experimental and transitional forms of direct–indirect–hybrid distribution under platform dependence
  • Governance implications of coordinating online and offline channels within platform-mediated systems
  • Boundary decisions under AI-enabled intermediation (e.g., AI-based pricing tools, automated advertising systems)

3. Ecosystem power, dependence, and the micro-foundations of value capture

  • How governance mechanisms redistribute bargaining power and strategic discretion across ecosystem actors
  • Vendor strategies for managing dependence, asymmetry, and multi-homing under algorithmic control
  • Long-term consequences of marketplace participation for autonomy, growth trajectories, and competitive positioning
  • Strategic implications of algorithmic visibility and attention allocation (e.g., ranking and recommendation regimes)

4. Capabilities, alliances, and organizational learning under platform governance

  • How firms build governance-specific capabilities to operate under rule-based and algorithmic coordination
  • Alliances, relational contracts, and tacit learning as complements or substitutes for formal governance
  • Co-evolution of corporate transformation and ecosystem-level change in platform settings
  • Learning mechanisms for adapting to AI-enabled governance and platform policy changes

5. AI-enabled governance and the transformation of managerial control

  • How algorithmic and generative-AI systems reshape monitoring, ranking, enforcement, and coordination practices
  • Managerial sense-making, learning, and resistance in the adoption of AI for governance and ecosystem management
  • Strategic consequences of opacity, automation, and explainability for trust, legitimacy, and accountability
  • Ethical and societal implications of AI-enabled governance (e.g., fairness, contestability, transparency)
  • The impact of GenAI on ranking algorithms
  • Governance of cross-border data in platforms

6. Ecosystem orchestration, global expansion, and institutional complexity

  • Governance challenges in platform-enabled global value chains and cross-border ecosystems
  • Orchestration strategies for resilience, reconfiguration, and ecosystem renewal under technological turbulence
  • Strategic responses to regulatory fragmentation and institutional pluralism in digital marketplaces
  • AI-enabled orchestration capabilities and ecosystem coordination across borders

Deadline and Submission Details

The submission will open May 10, 2027

The submission deadline for all papers is July 10, 2027

The publication date of this special issue is January 31, 2028

To submit your research, please visit the Scholar One manuscript portal. https://mc.manuscriptcentral.com/jsma 

To view the author guidelines for this journal, please visit the journal’s page. https://www.emeraldgrouppublishing.com/journal/jsma 

Contact the Guest Editor:

Ciro Troise, Department of Management, University of Turin, Ciro.troise@unito.it 

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May 27, 2026 · 9:04 am

Explainable AI: What’s inside the black box?

by Mark Anthony Camilleri | May 27th 2026

Artificial intelligence (AI) is now part of everyday life. It recommends what we watch online, helps banks approve loans, assists doctors in hospitals and even acts as a digital gatekeeper for who gets hired. Many people enjoy the convenience of these systems, yet few truly understand how they work. That is where the “Explainable AI” notion comes in. Essentially, it is a growing movement that is aimed at increasing AI transparency, to earn user trust.

For years, AI systems were treated like mysterious “black boxes”. You feed information into them and they produce an answer. However, at times, it proves hard to clearly explain how they have reached their conclusions. Even the engineers who have built these systems sometimes struggle to fully understand the internal reasoning behind complex AI models.

This becomes worrying when AI is used in areas such as healthcare, education, banking, policing or public services. Imagine applying for a loan and being rejected by an AI system without any explanation. Alternatively, consider a hospital using AI to help doctors diagnose patients without anyone being able to explain why the system recommended a particular treatment. In such situations, people may naturally ask: Why did the machine decide this? Explainable AI (XAI) tries to answer that question.

The basic idea is simple. AI systems should not only give answers; they should also explain how they arrived at them. Users deserve understandable reasons behind decisions that affect their lives. Transparency builds trust. Without it, people may fear that AI is unfair, biased or unreliable.

These concerns are not imaginary. There have already been cases where AI systems produced erroneous results because they learned from flawed or incomplete data. Some systems have treated people unfairly because of gender, race, age or where they resided. If the data used to train an AI system contains bias, the machine will usually amplify it.

Notwithstanding, the world around us never stands still. Economies shift, behaviours evolve and social conditions change. As a result, the AI models that are trained on old data may become inaccurate over time. Hence, an AI system that worked well two years ago may suddenly start making poor or unfair decisions today. Experts call this “data drift” or “concept drift”.

This is why explainability matters so much. When AI systems can be examined and understood, it is much easier to detect and correct their errors and biases.

Researchers and technology companies are already developing tools to make AI more understandable. Some of these diagnostic tools have unusual names such as SHAP and LIME. Despite the technical labels, their purpose is quite straightforward: These interpretability frameworks can help identify which factors have influenced an AI decision the most.

For example, if an AI system denies someone a bank loan, these tools can show whether income, employment history or debt level has played the biggest role in the decision. This allows humans to review whether the outcome was fair and reasonable.

In this day and age, explainable AI has moved beyond the lab; it is now a concern for everyone, not just for tech experts. Regulators, governments and businesses are demanding for more transparency. In Europe, the new AI Act and existing privacy laws such as GDPR are pushing organisations to become more accountable for how AI systems operate.

There is also growing recognition that humans must remain involved in important decisions. Experts often refer to this as the “human-in-the-loop” approach. In simple terms, AI is meant to support human judgement. It should not replace it. A doctor, teacher, judge or manager should still be able to question and override an AI recommendation when necessary.

This balance is essential because AI systems are powerful, but they are not infallible. They can make mistakes, misunderstand situations, hallucinate or fail to recognise unique human circumstances.

We simply cannot afford to trust algorithms blindly. This is where explainable AI steps in. It helps ordinary users feel more confident about the technology they use every day. When people understand how a system works, they are far more likely to accept it. Thus, transparency will replace fear and confusion.

However, the challenge is that there is often a trade-off between power and simplicity. The most advanced AI systems, including modern generative AI tools, are often the hardest to explain. Simpler systems are easier to understand but may not perform as well. Therefore, researchers are striving in their endeavours to find the right balance between accuracy and transparency.

Arguably, the future of AI hinges on trust. Society is unlikely to fully embrace technologies that appear secretive or uncontrollable. Businesses and governments must therefore ensure that AI systems are fair, explainable and aligned with human values.

If these systems remain opaque, as  their modus operandi are hidden, blurry or impossible to see through, their users may lose confidence in them. On the other hand, if we make AI understandable, we can finally harness its full power to build a fairer, more beneficial future for all.

Debatably, explainable AI is more than a technical upgrade. It is a moral safeguard. It ensures that humans don’t get sidelined as machines become smarter.

In this new information era, explainable AI isn’t just a technological upgrade; it’s a moral boundary. It ensures that as machines get smarter, humans don’t get left in the dark. In a world shaped by intelligent machines, we must hold on to one simple rule: if an algorithm makes a choice that changes your life, you have every right to know how it reached its conclusion, why that decision was made, where the data came from and when the logic was applied.

Learn about Explainable AI. You may refer to my open access article that was published through Elsevier’s Technological Forecasting and Social Change (ABS 3; ABDC A). It advances a systematic review of leading explanable artificial intelligence (XAI) tools, frameworks and best practices.

Key takeaways:

📍It explains key concepts related to XAI research.

📍It provides clear insights into widely used techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), among others.

📍It presents a comparison matrix of XAI tools. It specifies their key metrics, strengths, weaknesses/limitations and domain fit.

📍It puts forward a conceptual framework to support responsible AI implementation.

📍It provides practical, actionable guidance for developers of AI solutions, as well as for professionals, who are responsible for managing data-driven strategies and governance policies.

📍It serves as a valuable resource for those aiming to move beyond black-box reliance toward more informed, responsible and accountable AI oversight.

📍It outlines future research directions related to XAI and discusses on their potential impact.

Suggested Citation: Camilleri, M.A. (2026). Opening the black box: Operational principles, tools and frameworks that advance explainable artificial intelligence (XAI) models, Technological Forecasting and Social Change, https://doi.org/10.1016/j.techfore.2026.124710

Mark Anthony Camilleri is an Associate Editor of Bus. Strat. & the Environ. of the Int. J. of Hosp. Mgt.| Fulbrighter| Listed among top 2% of scientists (Elsevier)| Expert Reviewer for research councils| Principal Investigator| Statistician| PhD Mentor

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October 20, 2025 · 2:03 pm

Call for papers: Community-driven (Social) Innovation in Collaborative Ecosystems

I am delighted to share this call for papers for the European Academy of Management’s (EURAM2026’s) SIG01: Business for Society (B4S).

My colleagues, Mario Tani, University of Naples Federico II, Naples, Italy; Gianpaolo Basile, Università Telematica Universitas Mercatorum, Rome, Italy; Ciro Troise, University of Turin, Turin, Italy; Maria Palazzo, Università Telematica Universitas Mercatorum, Rome, Italy; Asha Thomas, Wrocław University of Science and Technology AND I, are guest editing a track entitled: “Relationships, Values, and Community-driven (Social) Innovation in Collaborative Ecosystems” (T01-14).

We are inviting conceptual, empirical and methodological papers on the interplay between open innovation, digital platforms and the power of the crowd in navigating today’s grand challenges.

“This track explores the strategic shift from firm-centric models to dynamic, collaborative ecosystems. We examine how deep stakeholder engagement, shared values, and community-driven innovation can generate sustainable economic, social, and environmental value”.

Further details about this conference track are available here: https://lnkd.in/djN8KpDw [T01-14].

Keywords: EURAM2026; Business For Society B4S; Collaborative Ecosystems; Open Innovation Community Driven Innovation; Stakeholder Engagement; Digital; Digital Platforms; Digital Transformation; Crowdsourcing; Sustainable Development Goals (SDGs); UNSDGs; SDG9 [Industry, Innovation And Infrastructure]; SDG11 [Sustainable Cities And Communities]; SDG12 [Responsible Consumption And Production]; SDG17 [Partnerships For The Goals].

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September 8, 2025 · 2:50 pm

Exaggerated statements in online consumer reviews: Causes and implications

Featuring snippets from an article that was accepted for publication through Springer’s “Service Business”.

Suggested citation: Camilleri, M.A., Bhatnagar, S.B. & Chakraborty, D. (2025). Exaggerated statements in online consumer reviews: Causes and implications. Service Business, 19, Art. 19, https://doi.org/10.1007/s11628-025-00590-6

Abstract

This study investigates the factors that contribute to the creation of inflated consumer testimonials. Quantitative data were gathered from four hundred forty (440) respondents who shared their service experiences through popular social media platforms. A covariance-based structural equations model approach has been used to analyze the data. The results suggest that psychological and emotional factors including the consumers’ self-image, self-enhancement as well as their motivations for retribution against service providers, are having a significant effect on the development of amplified review content.

Keywords: Consumer reviews, Constructive reviews, Altruistic reviews, Overblown reviews; tourism and hospitality.

1 Introduction

Researchers have frequently reported that certain individuals tend to misrepresent facts and may willingly decide to deceive other persons, in their daily conversations, including in virtual ones (Moqbel and Jain 2025; Sahut et al. 2024). It is very likely that such persons would fabricate content when they engage in online conversations (Plotkina et al. 2020) and may even create inflated claims in their user generated content, while sharing personal experiences with online users (Belarmino et al. 2022; Bozkurt et al. 2023). Electronic word of mouth communications, like online reviews, are not always truthful (Camilleri, 2022; Kapoor et al. 2021; Lee et al. 2022; Tomazelli et al. 2024), as they may frequently feature inflated claims (Román et al. 2023). A few researchers have even suggested that exaggerated reviews can have an adverse effect on their credibility (Chatterjee et al. 2023).

A lack of credibility and trustworthiness in online reviews could negatively affect the consumers’ perceptions and attitudes toward the business (Camilleri and Filieri 2023; Tan and Chen 2023). For instance, Fong et al. (2024) distinguished between trustworthy and untrustworthy content presented in online consumer testimonials. Yet, for the time being, there is still scarce research focused on the propagation of inflated claims in online reviews (Arif and Chandwani 2024). Various researchers have often attempted to find ways to detect misinformation and prefabricated online content including in social media and review platforms (Chen et al. 2022).

However, in many cases, it proves difficult to recognize the identities of those reviewers who are sharing overblown and deceitful statements about their experiences in online platforms (Bylok 2022). Notwithstanding, there may be different reasons why individuals engage in deceptive behaviors. People may decide to deceive others for personal gain, and/or to protect their own image or reputation. Their intention could be to manipulate others to achieve desired outcomes (Min and Wakslak 2022). Alternatively, they may rationalize their deceitful behaviors due to psychological factors. Such individuals would probably convince themselves that their actions are justified or harmless (Costa Filho et al. 2023; Petrescu et al. 2022).

Undoubtedly, the topic about deceitful, unreliable and inflated online reviews warrants further investigation, as these electronic word-of-mouth communications may constitute false advertising or fraud. Prospective consumers can be manipulated and misled into buying substandard or misrepresented products/services. For example, the use of generative AI could exacerbate the pervasiveness of fake inflated review content with high linguistic sophistication. Hence, it may prove hard for online users to detect the legitimacy and veracity of consumer reviews. Certainly, further investigation is warranted on this topic, to better understand the incidence and the scale of the exaggerated claims featured in user-generated content, their underlying motivations and drivers, as well as the identification of technological and regulatory responses.

In this light, this research identifies the factors and the extent to which online users share overstatements and amplified assertions in consumer review platforms. Specifically, the underlying research questions are: [RQ1] How and to what extent are the consumers’ altruistic intentions to provide customer-focused reviews contributing to the development of exaggerated claims in their testimonials? [RQ2] How and to what extent are the consumers’ constructive reviews aimed at service providers having an effect on the development of exaggerated claims in their testimonials? [RQ3] How and to what extent are the consumers’ psychological factors including their self-esteem and self-image having an effect on the development of exaggerated claims in their testimonials? [RQ4] How and to what extent are the consumers’ dissatisfaction levels with the services they receive and their retribution motivations having an effect on the development of exaggerated claims in their testimonials?

This empirical study builds on extant theoretical underpinnings related to the interpersonal deception theory (Buller and Burgoon 1996; Buller et al. 1996; Burgoon 2015; Gaspar et al. 2022) to delve into the factors that can lead consumers to create inflated claims in online reviews (Hill Cummings et al. 2024; Valdez et al. 2018). The researchers validate constructs that were tried and tested in academia including altruistic motivations to support prospects and/or businesses (Hennig-Thurau et al. 2004; Yoo and Gretzel 2008), perceived self-enhancement, perceived self-image and retribution behaviors (Yoo and Gretzel 2008).

Unlike previous studies, that focus on how reviews could influence purchase decisions, or those that investigate the rationale for sharing reviews, this contribution examines the processes and motivations that lead to the articulation of exaggerated claims in testimonials (that can be either positive or negative). From the outset, this original research rejects the dominant assumption that inflated reviews are simply driven by the consumers’ egos, or from their malicious intentions. On the contrary, it suggests that altruistic appraisals that are meant to support prospective customers, constructive criticism to service providers or feedback motivated by retributive intentions, after experiencing service failures, and/or the integration of psychological self-concepts could amplify or trigger exaggerated claims in consumer reviews. As far as the authors are aware, for the time being, there are no other studies that have integrated the above factors in the same conceptual model by referring to the interpersonal deception theory as an exploratory lens. Therefore, this contribution aims to address this knowledge gap, in the tourism and hospitality industry context. The study advances a novel theoretical model that is empirically tested, in terms of the constructs’ reliabilities and validities. Moreover, it also sheds light on the significance of the causal paths that predict the consumers’ likelihood of creating exaggerated content in review platforms.

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March 18, 2025 · 6:29 pm

Scaling up small enterprises: What’s the growth formula?

Pleased to share that I have recently coauthored an open-access article about the growth hacking capabilities of small and medium-sized enterprises (SMEs). It has been published in collaboration with my Italian colleagues from the University of Turin, via the Journal of Business Research.

Our research confirms that SMEs can leverage their growth potential through return-generating investments in disruptive innovations and by harnessing big data analytics. In sum, it suggests that core competencies, resources, and capabilities in these areas, can enhance the SMEs’ financial and operational performance.

READ FURTHER: The full paper can be accessed here: https://www.sciencedirect.com/science/article/pii/S0148296325001110

Suggested citation: Giordino, D., Troise, C., Bresciani, S. & Camilleri, M.A. (2025). Growth hacking capability: Antecedents and performance implications in the context of SMEs, Journal of Business Research, 192, https://doi.org/10.1016/j.jbusres.2025.115288 

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